Abstract
BackgroundGene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more. In the present study we focus on coding sequence modeling algorithms; that is, algorithms for identification and prediction of the actual coding sequences from genomic DNA. In this respect, we promote a novel multivariate method known as Canonical Powered Partial Least Squares (CPPLS) as an alternative to the commonly used Interpolated Markov model (IMM). Comparisons between the methods were performed on DNA, codon and protein sequences with highly conserved genes taken from several species with different genomic properties.ResultsThe multivariate CPPLS approach classified coding sequence substantially better than the commonly used IMM on the same set of sequences. We also found that the use of CPPLS with codon representation gave significantly better classification results than both IMM with protein (p < 0.001) and with DNA (p < 0.001). Further, although the mean performance was similar, the variation of CPPLS performance on codon representation was significantly smaller than for IMM (p < 0.001).ConclusionsThe performance of coding sequence modeling can be substantially improved by using an algorithm based on the multivariate CPPLS method applied to codon or DNA frequencies.
Highlights
Gene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more
A recent advancement to the PLS regression scheme combines a novel data compression method, canonical correlation analysis (CCA), to estimate latent variables enhancing classification in regression type problems even further. This method has been termed Canonical Powered Partial Least Squares (CPPLS) [22] and we explore the performance on the modeling of coding genes
For further understanding why a multivariate approach like CPPLS outperforms Interpolated Markov model (IMM), we have focused on the results for Sulfolobus islandicus, with codon representation
Summary
Gene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more. In the present study we focus on coding sequence modeling algorithms; that is, algorithms for identification and prediction of the actual coding sequences from genomic DNA. In this respect, we promote a novel multivariate method known as Canonical Powered Partial Least Squares (CPPLS) as an alternative to the commonly used Interpolated Markov model (IMM). A complicating factor is that a fraction of microbial genomes consist of degenerated genes with no remaining functionality [2]. A gene finder must be a rather complex ’engine’ capable of distinguishing real protein-coding genes from DNA sequence regions consisting of degenerated genes, non-coding regions and more. The number of ORFs found by gene finders is typically large compared to the true number
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